A FRAMEWORK FOR GENERATING BINARY SPLITS IN DECISION TREES
Felipe de A. Mello Pereira · 2018
In this dissertation we propose a framework for designing splitting criteria for handling multi-valued nominal attributes for decision trees.Criteria derived from our framework can be implemented to run in polynomial time in the number of classes and values, with theoretical guarantee of producing a split that is close to the optimal one.We also present an experimental study, using real datasets, where the running time and accuracy of the methods obtained from the framework are evaluated.